Speed of information delivery is a valuable dimension to the financial instrument trading and brokerage industry. The ability of a trader to obtain pricing information on financial instruments such as stocks, bonds and particularly options as quickly as possible cannot be understated; improvements in information delivery delay on the order of fractions of a second can provide important value to traders.
For example, suppose there is an outstanding “bid” on stock X that is a firm quote to buy 100 shares of Stock X for $21.50 per share. Also suppose there are two traders, A and B, each trying to sell 100 shares of stock X, but would prefer not to sell at a price of $21.50. Next, suppose another party suddenly indicates a willingness to buy 100 shares of Stock X for a price of $21.60. A new quote for that amount is then submitted, which sets the “best bid” for Stock X to $21.60, up 10 cents from its previous value of $21.50. The first trader, A or B, to see the new best bid price for Stock X and issue a counter-party order to sell Stock X will “hit the bid”, and sell his/her Stock X for $21.60 per share. The other trader will either have to settle for selling his/her shares of Stock X for the lower $21.50 price or will have to decide not to sell at all at that lower price. Thus, it can be seen that speed of information delivery can often translate into actual dollars and cents for traders, which in large volume situations, can translate to significant sums of money.
In an attempt to promptly deliver financial information to interested parties such as traders, a variety of market data platforms have been developed for the purpose of ostensible “real time” delivery of streaming bid, offer, and trade information for financial instruments to traders. FIG. 1 illustrates an exemplary platform that is currently known in the art. As shown in FIG. 1, the market data platform 100 comprises a plurality of functional units 102 that are configured to carry out data processing operations such as the ones depicted in units 102, whereby traders at workstations 104 have access to financial data of interest and whereby trade information can be sent to various exchanges or other outside systems via output path 110. The purpose and details of the functions performed by functional units 102 are well-known in the art. A stream 106 of financial data arrives at the system 100 from an external source such as the exchanges themselves (e.g., NYSE, NASDAQ, etc.) over private data communication lines or from extranet providers such as Savvis or BT Radians. The financial data source stream 106 comprises a series of messages that individually represent a new offer to buy or sell a financial instrument, an indication of a completed sale of a financial instrument, notifications of corrections to previously-reported sales of a financial instrument, administrative messages related to such transactions, and the like. As used herein, a “financial instrument” refers to a contract representing equity ownership, debt or credit, typically in relation to a corporate or governmental entity, wherein the contract is saleable. Examples of “financial instruments” include stocks, bonds, commodities, currency traded on currency markets, etc. but would not include cash or checks in the sense of how those items are used outside financial trading markets (i.e., the purchase of groceries at a grocery store using cash or check would not be covered by the term “financial instrument” as used herein; similarly, the withdrawal of $100 in cash from an Automatic Teller Machine using a debit card would not be covered by the term “financial instrument” as used herein). Functional units 102 of the system then operate on stream 106 or data derived therefrom to carry out a variety of financial processing tasks. As used herein, the term “financial market data” refers to the data contained in or derived from a series of messages that individually represent a new offer to buy or sell a financial instrument, an indication of a completed sale of a financial instrument, notifications of corrections to previously-reported sales of a financial instrument, administrative messages related to such transactions, and the like. The term “financial market source data” refers to a feed of financial market data directly from a data source such as an exchange itself or a third party provider (e.g., a Savvis or BT Radianz provider). The term “financial market secondary data” refers to financial market data that has been derived from financial market source data, such as data produced by a feed compression operation, a feed handling operation, an option pricing operation, etc.
Because of the massive computations required to support such a platform, current implementations known to the inventors herein typically deploy these functions across a number of individual computer systems that are networked together, to thereby achieve the appropriate processing scale for information delivery to traders with an acceptable degree of latency. This distribution process involves partitioning a given function into multiple logical units and implementing each logical unit in software on its own computer system/server. The particular partitioning scheme that is used is dependent on the particular function and the nature of the data with which that function works. The inventors believe that a number of different partitioning schemes for market data platforms have been developed over the years. For large market data platforms, the scale of deployment across multiple computer systems and servers can be physically massive, often filling entire rooms with computer systems and servers, thereby contributing to expensive and complex purchasing, maintenance, and service issues.
This partitioning approach is shown by FIG. 1 wherein each functional unit 102 can be thought of as its own computer system or server. Buses 108 and 110 can be used to network different functional units 102 together. For many functions, redundancy and scale can be provided by parallel computer systems/servers such as those shown in connection with options pricing and others. To the inventors' knowledge, these functions are deployed in software that is executed by the conventional general purpose processors (GPPs) resident on the computer systems/servers 102. The nature of general purpose processors and software systems in the current state of the art known to the inventors herein imposes constraints that limit the performance of these functions. Performance is typically measured as some number of units of computational work that can be performed per unit time on a system (commonly called “throughput”), and the time required to perform each individual unit of computational work from start to finish (commonly called “latency” or delay). Also, because of the many physical machines required by system 100, communication latencies are introduced into the data processing operations because of the processing overhead involved in transmitting messages to and from different machines.
Despite the improvements to the industry that these systems have provided, the inventors herein believe that significant further improvements can be made. In doing so, the inventors herein disclose that the underlying technology disclosed in the related patents and patent applications listed and incorporated herein above to fundamentally change the system architecture in which market data platforms are deployed.
In above-referenced related patent application Ser. No. 10/153,151, it was first disclosed that reconfigurable logic, such as Field Programmable Gate Arrays (FPGAs), can be deployed to process streaming financial information at hardware speeds. As examples, the application Ser. No. 10/153,151 disclosed the use of FPGAs to perform data reduction operations on streaming financial information, with specific examples of such data reduction operations being a minimum price function, a maximum price function, and a latest price function. (See also the above-referenced and incorporated patent application Ser. No. 11/561,615).
Since that time, the inventors herein have greatly expanded the scope of functionality for processing streams of financial information with reconfigurable logic. With the invention described herein, vast amounts of streaming financial information can be processed with varying degrees of complexity at hardware speeds via reconfigurable logic deployed in hardware appliances that greatly consolidate the distributed GPP architecture shown in FIG. 1 such that a market data platform built in accordance with the principles of the present invention can be implemented within fewer and much smaller appliances while providing faster data processing capabilities relative to the conventional market data platform as illustrated by FIG. 1; for example, the inventors envision that a 5:1 or greater reduction of appliances relative to the system architecture of FIG. 1 can be achieved in the practice of the present invention.
As used herein, the term “general-purpose processor” (or GPP) refers to a hardware device that fetches instructions and executes those instructions (for example, an Intel Xeon processor or an AMD Opteron processor). The term “reconfigurable logic” refers to any logic technology whose form and function can be significantly altered (i.e., reconfigured) in the field post-manufacture. This is to be contrasted with a GPP, whose function can change post-manufacture, but whose form is fixed at manufacture. The term “software” will refer to data processing functionality that is deployed on a GPP. The term “firmware” will refer to data processing functionality that is deployed on reconfigurable logic.
Thus, as embodiments of the present invention, the inventors herein disclose a variety of data processing pipelines implemented in firmware deployed on reconfigurable logic, wherein a stream of financial data can be processed through these pipelines at hardware speeds.
Also disclosed as an embodiment of the invention is a ticker plant that is configured to process financial market data with a combination of software logic and firmware logic. Through firmware pipelines deployed on the ticker plant and efficient software control and management over data flows to and from the firmware pipelines, the inventors herein believe that the ticker plant of the preferred embodiment is capable of greatly accelerating the speed with which financial market data is processed. In a preferred embodiment, financial market data is first processed within the ticker plant by software logic. The software logic controls and manages the flow of received financial market data into and out of the firmware logic deployed on the reconfigurable logic device(s), preferably in a manner such that each financial market data message travels only once from the software logic to the firmware logic and only once from the firmware logic back to the software logic. As used herein, the term “ticker plant” refers to a plurality of functional units, such as functional units 102 depicted in FIG. 1, that are arranged together to operate on a financial market data stream 106 or data derived therefrom.
These and other features and advantages of the present invention will be understood by those having ordinary skill in the art upon review of the description and figures hereinafter.